Search Results for author:
Found 7 papers, 0 papers with code
Machine Vision Based Assessment of Fall Color Changes in Apple Trees: Exploring Relationship with Leaf Nitrogen Concentration
The study involved the segmentation of the trees in a natural background using point cloud data and quantification of the color using a custom-defined metric, \textit{yellowness index}, varying from $-1$ to $+1$ ($-1$ being completely green and $+1$ being completely yellow), which gives the proportion of yellow leaves on a tree.
Comparing YOLOv8 and Mask RCNN for object segmentation in complex orchard environments
Mask R-CNN, in this single-class scenario, achieved a precision of 0. 85 and a recall of 0. 88.
Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques
This superiority is evident from the metrics: the RMSE values (2. 35 mm for Azure Kinect vs. 9. 65 mm for Realsense D435i), MAE values (1. 66 mm for Azure Kinect vs. 7. 8 mm for Realsense D435i), and the R-squared values (0. 9 for Azure Kinect vs. 0. 77 for Realsense D435i).
Real-time Strawberry Detection Based on Improved YOLOv5s Architecture for Robotic Harvesting in open-field environment
This study proposed a YOLOv5-based custom object detection model to detect strawberries in an outdoor environment.
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model
Similar to these measures, human evaluation also showed that images generated using image-to-image-based method were more realistic compared to those generated with text-to-image approach.
Machine Vision System for Early-stage Apple Flowers and Flower Clusters Detection for Precision Thinning and Pollination
Early-stage identification of fruit flowers that are in both opened and unopened condition in an orchard environment is significant information to perform crop load management operations such as flower thinning and pollination using automated and robotic platforms.
Swin-transformer-yolov5 For Real-time Wine Grape Bunch Detection
Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied models for grape bunch detection, with up to 97% of mean Average Precision (mAP) and 0. 89 of F1-score when the weather was cloudy.
